This is the era of Big Data. We are surrounded by cloud computing and enormous amounts of data are being processed and stored in data centers. For fast data retrieval and interactive and fast web services, NoSQL databases (or key-value stores) are prevalent. For example, Facebook and other SNS providers commonly deploy hundreds and thousands of such services. In particular, memcached database caching software is popular for its fast accesses and light implementations.
There are huge gaps in the average performance, cost, and energy gaps in existing single-layer caching-with-backend database architecture. For example, an average access or service time for memcached software is around 10-20 micro seconds, whereas a backend SQL database query may take 20 milliseconds: up to 2000 times slower. Decent servers for running SQL databases could easily cost $30,000.00 dollars. Power consumptions for different servers also vary significantly. For example, small servers could burn 200 watts while large servers may burn as much as 1,500 watts. Nor is it effective to increase the memory capacity of the server: memory is itself very expensive and requires significant power.
A need remains for a way to improve the performance of large databases and to balance the two objectives of minimizing latency and keeping cost down.